






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__a:     List[str]   					=					logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    UniSpeechSatForSequenceClassification.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""projector.weight"""]
       _UpperCAmelCase							=    downstream_dict["""projector.bias"""]
       _UpperCAmelCase							=    downstream_dict["""model.post_net.linear.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.post_net.linear.bias"""]
       return model




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       _UpperCAmelCase							=    UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""model.linear.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.linear.bias"""]
       return model




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    UniSpeechSatForXVector.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""connector.weight"""]
       _UpperCAmelCase							=    downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
              _UpperCAmelCase							=    downstream_dict[
                  f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
              ]
              _UpperCAmelCase							=    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       _UpperCAmelCase							=    downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )

       _UpperCAmelCase							=    checkpoint["""Downstream"""]

       _UpperCAmelCase							=    UniSpeechSatConfig.from_pretrained(__snake_case )
       _UpperCAmelCase							=    WavaVecaFeatureExtractor.from_pretrained(
           __snake_case   ,      return_attention_mask=__snake_case   ,      do_normalize=__snake_case )

       _UpperCAmelCase							=    hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification""" ):
              _UpperCAmelCase							=    convert_classification(__snake_case   ,      __snake_case   ,      __snake_case )
       elif arch.endswith("""ForAudioFrameClassification""" ):
              _UpperCAmelCase							=    convert_diarization(__snake_case   ,      __snake_case   ,      __snake_case )
       elif arch.endswith("""ForXVector""" ):
              _UpperCAmelCase							=    convert_xvector(__snake_case   ,      __snake_case   ,      __snake_case )
       else:
              raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""" )

       if hf_config.use_weighted_layer_sum:
              _UpperCAmelCase							=    checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(__snake_case )
       hf_model.save_pretrained(__snake_case )


if __name__ == "__main__":
  __a:     Any   					=					argparse.ArgumentParser()
  parser.add_argument(
      '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
  )
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
  parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
  __a:     Optional[Any]   					=					parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


